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Purpose: The purpose of this review was to evaluate the effects of physical activity on children's free recall, cued recall, and recognition episodic memory and to explore potential moderating factors. Methods: The following databases were searched: PubMed, ERIC, APA Psych Info, CINHAL, SPORTDiscus, and Google Scholar. Studies were included if: (1) participants were aged 4–18 years, (2) participants were typically developed, (3) participants were randomized to groups, (4) interventions employed gross movements, (5) sedentary group was used for control, (6) memory tests were quantitative, and (7) employed acute or chronic intervention. Results: 14 studies met inclusion criteria resulting in the analysis of data from 7 free recall, 7 cued recall, and 8 recognition memory tests. Physical activity was found to have a positive influence on tests free (g = 0.56), cued recall (g = 0.67), and no influence on tests of recognition (g = 0.06). While some moderator analyses were significant, the authors do not consider these results to be meaningful in application. Conclusions: The effects of acute and chronic physical activity enhance specific aspects of long-term episodic memory. These findings suggest physical activity interventions developed for children may be expected to benefit some, but not all, types of memory processing. [ABSTRACT FROM AUTHOR]

Anti-predator behaviours in response to predator cues can be innate, or they can be learned through prior experience and remembered over time. The duration and strength of continued anti-predator behaviour after predator cues are no longer present, and the potential for an enhanced response when re-exposed to predator cues later is less known but could account for the observed variation in anti-predator responses. We measured the carryover effects of past predation exposure and the potential for anti-predator learning and memory in the marine snail Nucella canaliculata from six populations distributed over 1000 km of coastline. We exposed lab-reared snails to cues associated with a common crab predator or seawater control in two serial experiments separated by over seven months. Responses were population- and sex-dependent, with some populations retaining anti-predator behaviours while others showed a capacity for learning and memory. Male snails showed a strong carryover of risk aversion, while females were able to return to normal feeding rates and grow more quickly. These behavioural differences culminated in strong impacts on feeding and growth rates, demonstrating that this variation has implications for the strength of trait-mediated indirect interactions, which can impact entire ecosystems.

Nanowires (NWs) composed of 2,4-bis[(4-diethylamino)-2-hydroxyphenyl] squaraine were prepared by evaporation-induced self-assembly (EISA). NWs were ∼560 nm wide (aspect ratios: 10–90). X-ray diffraction analysis indicated polymorphism (monoclinic/triclinic). Optical data reported the triclinic phase with energetic disorder. Given the favorable alignment of the Au work function and squaraine HOMO energy, symmetric, unipolar metal–insulator–metal devices were formed by the EISA of NW meshes on inter-digitated Au electrodes. Room temperature DC I–V characteristics displayed hysteretic I–V loops, indicating memristive behavior. At low bias, data indicated Ohmic transport with carrier extraction facilitated by thermionic emission. At high biases, devices exhibited space-charge-limited conduction in the presence of shallow traps. At 77 K, data indicated Ohmic transport at low bias with carrier extraction by thermionic emission while, at high biases, trap-limited space-charge-limited conduction in the presence of traps distributed in energy, with carrier extraction by Fowler–Nordheim tunneling, was observed. The I–V hysteresis was eliminated at 77 K and attenuated by fast scan rates at room temperature, suggesting that carrier trapping/de-trapping underpinned the hysteresis. In impedance measurements, the device response fitted a Randles equivalent circuit indicating purely electronic conduction. By applying voltage waveforms, I–V hysteresis and analog resistive switching (memristive) functionality were observed. Device conductance could be increased sweep by sweep, giving conductance tuning through distinct states, with wait time- or voltage-erase options, consistent with trap filling/emptying effects. Repeated erase–write–read of multiple distinct states over many voltage cycles during continuous use in air was demonstrated. Finally, synaptic functions, e.g., pulse-dependent plasticity, and short- to long-term memory transition, were successfully emulated. [ABSTRACT FROM AUTHOR]

Optoelectronic synapses are inevitable for realizing neuromorphic vision systems, which require the integration of image recognition, memory and image processing into a single platform. In this work, we present a three terminal optoelectronic synapse created using zinc oxide (ZnO) thin film transistor. The persistent photoconductivity (PPC) of ZnO thin film is utilized to demonstrate the synaptic behavior. The change in conductance of the device under UV illumination has been interpreted as the weight change in the synapse. The basic synaptic functions such as sensory memory, short term memory, long term memory, duration-time-dependent plasticity and paired pulse facilitation (PPF) have been successfully demonstrated. The device shows a PPF index of 160%, comparable to other optoelectronic synapses reported in literature. Further, to corroborate the existing theory that PPC is caused by oxygen vacancies, additional characterizations are carried out and the presence of oxygen vacancies is detected in the fabricated ZnO device. Subsequently, pattern recognition of MNIST handwritten dataset has been performed using the conductance tuning curves of the proposed ZnO TFT based synapses in a neural network architecture, thereby demonstrating their feasibility to be used in neuromorphic applications. [ABSTRACT FROM AUTHOR]

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively. [ABSTRACT FROM AUTHOR]

A short-term power load forecasting method is proposed based on an improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), and Bidirectional Long Short Term Memory (BiLSTM) neural network. First, the SSA is optimized by combining Tent chaotic mapping, reverse learning, and dynamic step adjustment strategy, and the VMD mode number and penalty factor are optimized by ISSA. Secondly, the initial load sequence is decomposed into several Intrinsic Mode Function (IMF) components using ISSA-VMD. The effective modal components are screened by Wasserstein Distance (WD) between IMF and the original signal probability density. Then, the effective modal components are reconstructed by the Improved Multi-scale Fast Sample Entropy (IMFSE) algorithm. Finally, the extracted features and IMF were input into the ISSA-BiLSTM model as input vectors for prediction. [ABSTRACT FROM AUTHOR]

Prior work has shown Americans have higher levels of memory specificity than East Asians. Neuroimaging studies have not investigated mechanisms that account for cultural differences at retrieval. In this study, we use fMRI to assess whether mnemonic discrimination, distinguishing novel from previously encountered stimuli, accounts for cultural differences in memory. Fifty-five American and 55 Taiwanese young adults completed an object recognition paradigm testing discrimination of old targets, similar lures and novel foils. Mnemonic discrimination was tested by comparing discrimination of similar lures from studied targets, and results showed the relationship between activity in right fusiform gyrus and behavioural discrimination between target and lure objects differed across cultural groups. Parametric modulation analyses of activity during lure correct rejections also indicated that groups differed in left superior parietal cortex response to variations in lure similarity. Additional analyses of old vs. new activity indicated that Americans and Taiwanese differ in the neural activity supporting general object recognition in the hippocampus, left inferior frontal gyrus and middle frontal gyrus. Results are juxtaposed against comparisons of the regions activated in common across the two cultures. Overall, Americans and Taiwanese differ in the extent to which they recruit visual processing and attention modulating brain regions. [ABSTRACT FROM AUTHOR]

Procedural memory is involved in the acquisition and control of skills and habits that underlie rule and procedural learning, including the acquisition of grammar and phonology. The serial reaction time task (SRTT), commonly used to assess procedural learning, has been shown to have poor stability (test–retest reliability). We investigated factors that may affect the stability of the SRTT in adults. Experiment 1 examined whether the similarity of sequences learned in two sessions would impact stability: test–retest correlations were low regardless of sequence similarity (r <.31). Experiment 2 added a third session to examine whether individual differences in learning would stabilise with further training. There was a small (but nonsignificant) improvement in stability for later sessions (Sessions 1 and 2: r =.42; Sessions 2 and 3: r =.60). Stability of procedural learning on the SRTT remained suboptimal in all conditions, posing a serious obstacle to the use of this task as a sensitive predictor of individual differences and ultimately theoretical advance. [ABSTRACT FROM AUTHOR]

It has been hypothesized that differential processing occurs along the longitudinal (anterior–posterior) axis of the hippocampus. One hypothesis is that spatial memory (during both encoding and retrieval) is associated with the posterior hippocampus. An alternative hypothesis is that memory encoding (either spatial or nonspatial) is associated with the anterior hippocampus and memory retrieval is associated with the posterior hippocampus. Of importance, during spatial memory encoding, the spatial–posterior hypothesis predicts posterior hippocampal involvement, whereas the encoding–retrieval hypothesis predicts anterior hippocampal involvement. To distinguish between these hypotheses, we conducted a coordinate‐based fMRI activation likelihood estimation (ALE) meta‐analysis of 26 studies (with a total of 435 participants) that reported hippocampal activity during spatial memory encoding and/or spatial memory retrieval. Both spatial memory encoding and spatial memory retrieval produced extensive activity along the longitudinal axis of the hippocampus as well as the entorhinal cortex, the perirhinal cortex, and the parahippocampal cortex. Critically, the contrast of spatial memory encoding and spatial memory retrieval produced activations in both the anterior hippocampus and the posterior hippocampus. That spatial memory encoding produced activity in both the anterior and posterior hippocampus can be taken to reject strict forms of the spatial–posterior hypothesis, which stipulates that all forms of spatial memory produce activity in the posterior hippocampus, and the encoding–retrieval hypothesis, which stipulates that all forms of encoding versus retrieval produce activity in only the anterior hippocampus. Our results indicate that spatial memory encoding can involve the anterior hippocampus and the posterior hippocampus. [ABSTRACT FROM AUTHOR]

Every day, we encounter far more information than we could possibly remember. Thus, our memory systems must organize and prioritize the details from an experience that can adaptively guide the storage and retrieval of specific episodic events. Prior work has shown that shifts in internal goal states can function as event boundaries, chunking experiences into distinct and memorable episodes. In addition, at short delays, memory for contextual information at boundaries has been shown to be enhanced compared with items within each event. However, it remains unclear if these memory enhancements are limited to features that signal a meaningful transition between events. To determine how changes in dynamic goal states influence the organization and content of long-term memory, we designed a 2-day experiment in which participants viewed a series of black-and-white objects surrounded by a color border on a two-by-two grid. The location of the object on the grid determined which of two tasks participants performed on a given trial. To examine if distinct types of goal shifts modulate the effects of event segmentation, we changed the border color, the task, or both after every four items in a sequence. We found that goal shifts influenced temporal memory in a manner consistent with the formation of distinct events. However, for subjective memory representations in particular, these effects differed by the type of event boundary. Furthermore, to examine if goal shifts lead to the prioritization of goal-relevant features in longer lasting memories, we tested source memory for each object's color and grid location both immediately and after a 24-hr delay. On the immediate test, boundaries enhanced the memory for all concurrent source features compared with nonboundary items, but only if those boundaries involved a goal shift. In contrast, after a delay, the source memory was selectively enhanced for the feature relevant to the goal shift. These findings suggest that goals can adaptively structure memories by prioritizing contextual features that define a unique episode in memory. [ABSTRACT FROM AUTHOR]

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In Extended Conceptual Metaphor Theory (Kövecses, 2020), I offered a comprehensive overhaul of "standard" conceptual metaphor theory. The present paper attempts to demonstrate how the new view of CMT can handle metaphorical idioms. To this end, I analyze four metaphorical idioms from the thematic area of money (throw money about, money slips through someone's fingers, be a cash cow, money keeps someone/something afloat). The analysis assumes and starts out from the observation that money-related metaphors are based on two generic-level conceptual metaphors: money is a moving entity and money is a force. Extended CMT adds to CMT the notion of "mental space-level metaphors" that were largely ignored in "standard" CMT, but are given an important role in extended CMT. These are the metaphors that represent actual, contextual meanings in a metaphorical usage event. Traditional CMT-type analysis cannot account for the emergence of such idiomatic meanings because the mappings, or correspondences, of standard CMT work on a single, generic level (frame-, domain-, or even image schema level). However, the contextual meanings of naturally used metaphors (including those of metaphorical idioms) are much more information-rich and specific. The conceptual metaphors on the image schema, domain, and frame levels are offline structures in long-term memory, whereas the conceptual metaphors on the mental space level occur only online in working memory. In online communication, speakers mobilize the static image schema-, domain-, and frame-level metaphors at the mental space level, where they create highly specific mental space-level metaphors. Given this framework, it becomes possible to explain how and why the four metaphorical idioms have different contextual meanings (as represented by different mental space-level metaphors), but, at the same time, why they also share certain conceptual metaphors on the frame-, domain-, or image schema-level. Additionally, we gain new insight into how the emergence of novel metaphorical idioms occurs with the help of and constrained by a previously existing large system of hierarchically arranged conceptual metaphors. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the automated diagnosis of physiological curvature in the cervical spine, with an emphasis on feature point localization. Cervical spine deformity is prevalent, and the Cobb angle is widely recognized as the gold standard for diagnosing and treating it. However, manual measurement is time-consuming, labor-intensive, and heavily reliant on clinical experience. Therefore, there is an urgent need for a high-precision automatically detecting algorithm to meet the clinical requirements of orthopedic surgeons. Traditional methods are constrained by complex steps and limited data, which pose challenges. Therefore, we propose an efficient framework that formulates an automatic diagnosis of cervical spine physiological curvature based on a novel deep neural network. By leveraging the excellent properties of neural memory Ordinary Differential Equation (nmODE) in long-term memory retention and nonlinear representation capabilities, we effectively improve the network's performance in keypoint detection branching tasks. Additionally, we integrate a novel hybrid transformer based on residual structures and a multi-stage dilated dynamic convolution to alleviate false detections caused by X-ray obstruction and shadows, and the integration also captures the relationship between vertebrae and landmarks to compensate for the lack of detailed information. We constructed a dataset named CSL-947X, comprising 947 cervical spine lateral X-ray images of patients to train and evaluate our proposed model. Extensive experiments on CSL-947X demonstrate that our framework achieves higher accuracy and outperforms most state-of-the-art methods. These results highlight the effectiveness of the proposed architecture and its potential feasibility as a clinical decision-making tool for healthcare professionals. [ABSTRACT FROM AUTHOR]

Research Summary: External representations, particularly visuals, are important in strategic decision‐making. However, their pervasiveness and impact are not well understood in the strategy literature. Based on cognitive science research, we identify four cognitive functions crucial to strategic decision‐making that benefit from using external representations. We also propose a conceptual model and propositions that explain how the quality of strategic decision‐making depends on the interactions among task environment, external representations, and managers. We show that external representations influence in predictable ways the boundedly rational process of searching for new strategies. Key determinants include the manager's representational capability and the usability and malleability of the external representation. We discuss implications for users, designers, and teachers of external representations in strategy, as well as suggest avenues for future research. Managerial Summary: This research points to the pivotal role of external representations, especially visuals, in strategic decision‐making. Drawing from cognitive science, this study identifies four critical cognitive functions that benefit from these external representations—working memory, long‐term memory, pattern recognition, and knowledge transfer. Further, the study highlights that external representations significantly influence the process of strategic decision‐making in predictable ways. Finally, we show that not all external representations are alike in their ease of use and a managers' ability to operate on an external representation, referred to as representational capability, greatly affects the decision‐making quality. The implications extend to users, designers, and educators of external representations, urging attention to the design and use of external representations for improved decision outcomes. [ABSTRACT FROM AUTHOR]

The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications. The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions. On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach. The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition. Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data. In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification. The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model. The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger. The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19. The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR). The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method. The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL. [ABSTRACT FROM AUTHOR]

The paper introduces the VANET Mobility Regression Forecasting Network (VMRF-Net), a new model for effectively predicting vehicular mobility trends in VANETs (Vehicular adhoc Networks). VMRF-Net uses LSTM (Long Short Term Memory) layers with a 20% dropout rate to avoid overfitting and the 'adam' optimization method to improve learning efficiency. The suggested model produces remarkable results, including a 100% packet delivery ratio at lower node densities (20 and 40 nodes), as well as good performance at higher densities, with a minimum end-to-end delay of 0.29 seconds and a throughput of 1 at a node density of 20. Furthermore, the model shows a strong positive relationship between simulation duration and network sustainability, resulting in constant stability periods and longer operational lifetimes across different simulation lengths. These findings demonstrate the VMRF-Net's efficacy in improving VANET performance and resource utilization. [ABSTRACT FROM AUTHOR]

The article "Memory and Amnesia" in Frontiers in Education discusses a lesson plan for 7th and 8th-grade students to learn about different types of memory and amnesia. The lesson includes activities such as a working memory game, matching memories to their classifications, and analyzing memory loss in mock medical cases. The lesson aims to help students differentiate between short-term and long-term memory, understand different types of long-term memory, and recognize problems like anterograde and retrograde amnesia. The lesson also covers scientific vocabulary related to memory and provides resources for instructors to facilitate the activities effectively. [Extracted from the article]

Background: Developing interventions for older adults with subjective cognitive decline (SCD) has the potential to prevent dementia in this at-risk group. Preclinical models indicate that Citrus-derived phytochemicals could benefit cognition and inflammatory processes, but results from clinical trials are still preliminary. The aim of this study is to determine the effects of long-term supplementation with Citrus peel extract on cognitive performance and inflammation in individuals with SCD. Methods: Eighty participants were randomly assigned to active treatment (400 mg of Citrus peel extract containing 3.0 mg of naringenin and 0.1 mg of auraptene) or placebo at 1:1 ratio for 36 weeks. The primary endpoint was the change in the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score across the 36-week trial period. Other cognitive outcomes included tests and scales evaluating verbal memory, attention, executive and visuospatial functions, and memory concerns. The secondary endpoint was the change of interleukin-8 (IL-8) levels over the 36-week trial period in a subsample of 60 consecutive participants. An Intention-to-treat approach with generalized linear mixed models was used for data analysis. Results: The RBANS total score showed significant improvement in both Citrus peel extract and placebo groups at 36 weeks (p for time <.001, d = 0.36, p time x treatment =.910). Significant time effects were also found in cognitive domains of short- and long-term verbal memory (p <.001) and scales of subjective memory (p <.01), with no significant time x treatment interaction. The largest effect sizes were observed in verbal memory in the placebo group (d = 0.69 in short-term, and d = 0.78 in long-term verbal memory). Increased IL-8 levels were found at 36-week follow-up in both Citrus peel extract and placebo groups (p for time =.010, d = 0.21, p time x treatment =.772). Adverse events were balanced between groups. Conclusions: In this randomized clinical trial, long-term Citrus peel extract supplementation did not show cognitive benefits over placebo in participants with SCD, possibly due to high placebo response. These findings might have specific implications for designing future nutraceutical trials in individuals experiencing SCD. Trial registration: The trial has been registered at the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT04744922 on February 9th, 2021 (https://www.clinicaltrials.gov/ct2/show/NCT04744922). [ABSTRACT FROM AUTHOR]

Every second, a huge volume of multi-dimensional data is generated in fields such as Social Networking, Industrial Internet of Things, Stock market and E-commerce applications. Knowledge and pattern extraction are a challenging task in the evolving nature of data stream. Major issues are (i) ‘concept drift’ occurs as a result of pattern changes in the data distribution and (ii) ‘concept evolution’ occurs when a new class evolves in the data stream. These issues degrade the performance of learning models. In this paper, we focus on detection of concept evolution and enhance the performance of classifiers. For this, we propose a new model to identify novel classes, namely, Detection of Novel Classes (DNC). The proposed method adopts long short term memory to continuously observe the streaming data in order to detect emerging classes. The continuous monitoring allows the model to distinguish between existing classes and the novel classes which save time and memory. Also, the proposed method is demonstrated for identifying more than one novel class. The experiments are performed over seven different datasets. The results confirm the efficiency is increased ranging from 6% to 34% by the proposed method in identifying new concepts in the evolving data stream than the existing methods available in the literature. [ABSTRACT FROM AUTHOR]

Synaptic plasticity enables animals to adapt to their environment, but memory formation can require a substantial amount of metabolic energy, potentially impairing survival. Hence, a neuro-economic dilemma arises whether learning is a profitable investment or not, and the brain must therefore judiciously regulate learning. Indeed, in experiments it was observed that during starvation, Drosophila suppress formation of energy-intensive aversive memories. Here we include energy considerations in a reinforcement learning framework. Simulated flies learned to avoid noxious stimuli through synaptic plasticity in either the energy expensive long-term memory (LTM) pathway, or the decaying anesthesia-resistant memory (ARM) pathway. The objective of the flies is to maximize their lifespan, which is calculated with a hazard function. We find that strategies that switch between the LTM and ARM pathways, based on energy reserve and reward prediction error, prolong lifespan. Our study highlights the significance of energy-regulation of memory pathways and dopaminergic control for adaptive learning and survival. It might also benefit engineering applications of reinforcement learning under resources constraints. Author summary: There is increasing evidence that biological learning and in particular the creation of long lasting forms of memory requires substantial amounts of energy. It has been observed that as a result, animals such as drosophila might stop some forms of learning when they are low on energy. In this modelling paper we analyze this learning vs starvation trade-off using a hazard framework with as objective to maximize the lifetime of the animal. We then explore the optimal3 algorithm to balance energy saving with learning. We find that it is best to restrict the learning using expensive persistent memory to situations where the animal's energy reserve is high, and there is also a large deviation between expected and actual reward. We speculate that there is evidence for similar energy adaptive mechanism in mammalian learning. The findings might also be relevant for human behavior and artificial systems with resource limitations such as limited battery life. [ABSTRACT FROM AUTHOR]

Episodic autobiographical memory (EAM) is a long-term memory system of personally experienced events with their context – what, where, when – and subjective elements, e.g., emotions, thoughts, or self-reference. EAM formation has rarely been studied in a controlled, real-life-like paradigm, and there is no predictive model of long-term retrieval from self-rated subjective experience at encoding. The present longitudinal study, with three surprise free recall memory tests immediately, one-week and one-month after encoding, investigated incidental encoding of EAM in an immersive virtual environment where 30 participants either interacted with or observed specific events of varying emotional valences with simultaneous physiological recordings. The predictive analyses highlight the temporal dynamics of the predictors of EAM from subjective ratings at encoding: common characteristics related to sense of remembering and infrequency of real-life encounter of the event were identified over time, but different variables become relevant at different time points, such as the emotion and mental imagery or prospective aspects. This dynamic and time-dependent role of memory predictors challenges traditional views of a uniform influence of encoding factors over time. Current evidence for the multiphasic nature of memory formation points to the role of different mechanisms at play during encoding but also consolidation and subsequent retrieval. [ABSTRACT FROM AUTHOR]

Health concerns about excess dietary salt have traditionally focused on its relationship with hypertension and the increased risk of cognitive impairment. However, research has often overlooked the unique health concerns and physiological differences between men and women, leading to gaps in knowledge, particularly regarding disease prevention and treatment strategies for women. The present study examined aged female rats over 12 weeks, using control, low, and high salt diets to mimic the post-menopausal phase in human females when cardiovascular risks typically increase. Cardiometabolic parameters and cognition were monitored. The findings revealed the impact of varying salt diets on blood lipids, blood pressure (BP) and heart rate (HR) levels and variability, anxiety, and cognition. Specifically, intake of a low-salt diet led to a significant reduction in BP levels but an increase in BP variability starting from the eighth week of the diet onset. Moreover, HR levels and variability were notably higher with the low-salt diet. Aged female rats exhibited increased anxiety on the low-salt diet at the fourth week, but the anxiety began to improve starting from the eighth week. Additionally, a trend suggested that the low salt intake worsened short-term memory while improving long-term memory. Furthermore, plasma lipids decreased significantly in aged female rats on a high-salt diet compared to those on a low-salt diet. The study provides valuable insights into the effects of salt intake on cardiometabolic parameters and cognitive function in aged female rats, highlighting the importance of considering sex-specific dietary guidelines for cardiometabolic and cognitive health. [ABSTRACT FROM AUTHOR]

The combined physics-informed neural network is employed to deal with the free boundary problems of fractional Black-Scholes equations. The solution assumption and the loss function are determined, the transfer learning is borrowed, the combined neural network with data enhancement layer is designed, then the classical Black-Scholes model is numerically solved and the comparative analysis of numerical results under different neural networks is made. For further insight into the long-term memory of fluctuation, the free boundary problems of the space-time Black-Scholes equations under Caputo, Caputo-Fabrizio and Atangana-Baleanu-Caputo fractional derivatives are studied. The corresponding empirical analyses are presented and the optimal exercise boundaries of American put option are simulated. The market analysis shows that introducing fractional calculus tools and neural network algorithms into American put option pricing can yield more realistic prediction results. The work provides a viable method for subsequent researchers to study American option pricing using fractional calculus and neural networks combined with true market data and to deal with the free boundary problems in other research fields. [ABSTRACT FROM AUTHOR]

Recent findings suggest that prism adaptation can extend its effects beyond spatial attention, modulating the performance of different cognitive tasks by acting on cerebellar, parietal and temporal-frontal networks. We tested groups of healthy subjects to investigate the effects of rightward vs. leftward prism adaptation vs. neutral lenses exposure in a series of memory tasks, probing either short-term (Digit span, Corsi span) or long-term memory (Supraspan verbal and spatial learning). In the short-term memory tasks, leftward prism adaptation selectively increased verbal span, while rightward prism adaptation increased spatial span. In the long-term memory tasks, leftward prism adaptation selectively increased verbal supraspan, i.e., increased the number of digits in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. On the other hand, rightward prism adaptation selectively increased spatial supraspan, i.e. it increased the number of spatial positions in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. Moreover, rightward, but not leftward, prism adaptation selectively increased supraspan recall after a delay interval, regardless of the stimulus material, i.e., it increased the number of digits or spatial positions recalled after a delay interval. Neutral lenses exposure did not influence any memory task. These findings suggest that prism adaptation can induce both modality/hemispheric-specific and process-specific effects on short-term and long-term explicit memory. [ABSTRACT FROM AUTHOR]

Radiotherapy (RT), a common cancer treatment, unintentionally harms surrounding tissues, including the skin, and hinders wound healing years after treatment. This study aims to understand the mechanisms behind these late-onset adverse effects. We compare skin biopsies from previously irradiated (RT+) and non-irradiated (RT−) sites in breast cancer survivors who underwent RT years ago. Here we show that the RT+ skin has compromised healing capacity and fibroblast functions. Using ATAC-seq, we discover altered chromatin landscapes in RT+ fibroblasts, with THBS1 identified as a crucial epigenetically primed wound repair-related gene. This is further confirmed by single-cell RNA-sequencing and spatial transcriptomic analysis of human wounds. Notably, fibroblasts in both murine and human post-radiation wound models show heightened and sustained THBS1 expression, impairing fibroblast motility and contractility. Treatment with anti-THBS1 antibodies promotes ex vivo wound closure in RT+ skin from breast cancer survivors. Our findings suggest that fibroblasts retain a long-term radiation memory in the form of epigenetic changes. Targeting this maladaptive epigenetic memory could mitigate RT's late-onset adverse effects, improving the quality of life for cancer survivors. The molecular mechanisms underlying the late-onset adverse effects of radiotherapy remain to be explored. Here, the authors observe compromised wound healing capacity in irradiated skin from breast cancer survivors and highlight THBS1 as a key epigenetically primed wound repair-related gene. [ABSTRACT FROM AUTHOR]

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases. [ABSTRACT FROM AUTHOR]

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system. [ABSTRACT FROM AUTHOR]

The claustrum is a brain structure that remains shrouded in mystery due to the limited understanding of its cellular structure, neural pathways, functionality and physiological aspects. Significant research has unveiled connections spanning from the claustrum to the entire cortex as well as subcortical areas. This widespread connectivity has led to speculations of its role in integrating information from different brain regions, possibly contributing to processes such as attention, consciousness, learning and memory. Our working hypothesis posits that claustrum neural activity contributes to the acquisition, consolidation and reconsolidation of long-term memories in mice. We found evidence in CF-1 mice of a decline in behavioral performance in an inhibitory avoidance task due to intra-claustral administration of 2% lidocaine immediately after a training session or memory recall. Nevertheless, this does not seem to be the case for the acquisition or retrieval of this type of memory, although its neural activity is significantly increased after training, evaluated through c-Fos expression. Moreover, inhibition of the claustrum's synaptic activity appears to impair the consolidation but not acquisition or retrieval of an unconditioned memory formed in a nose-poke habituation task. [ABSTRACT FROM AUTHOR]

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance. [ABSTRACT FROM AUTHOR]

Objective: The aim of the current study was to determine the capability of neurocognitive variables and executive functions to differentiate women with and without fibromyalgia syndrome (FMS). Methods: A secondary diagnostic accuracy analysis was conducted. A battery of neurocognitive and executive function tests (the D2 Attention test, the Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit test for mental inhibition, the Symbol Search for processing speed, and the Zoo Test for planning/decision-making) were collected in 129 women with FMS and 111 without FMS. The area under the receiver operating characteristic (ROC) curve, optimal cut-off point, sensitivity, specificity, and positive and negative likelihood ratios (LR) for each variable were calculated. Results: Between-group differences were identified in ROCF_Copy (p = 0.043), ROCF_Recall (p = 0.004), d2_TR (p = 0.019), d2_TA (p = 0.007), d2_TOT (p = 0.005), d2_CON (p = 0.004), d2_C (p = 0.042), Symbol Search (p = 0.008), Decoding _FDT (p = 0.001), Retrieving_FDT (p = 0.001), and Inhibiting_FDT (p = 0.024). The result showed that FDT-based outcomes (Retrieving_FDT: ROC 0.739, sensitivity 85.3%, specificity 48.6%; Decoding_FDT: ROC 0.724, sensitivity 50.4%, specificity 16.2%; Inhibiting_FDT: ROC 0.708, sensitivity 56.6%, specificity 22.5%) were the variables able to differentiate between women with and without FMS. Conclusions: Although women with FMS exhibited deficits in attention, long-term visual memory, processing speed, and mental inhibition when compared with women without FMS, only mental inhibition scores showed moderate diagnostic accuracy to discriminate between women with and without FMS. Future studies investigating these results in clinical settings are needed to identify the clinical relevance of these findings. [ABSTRACT FROM AUTHOR]

In order to perform emotion prediction in music quickly and accurately, we have proposed a muscular and tall neural network architecture for music emotion classification. Specifically, during the audio pre-processing stage, we converge mel-scale frequency cepstral coefficients features and residual phase features with weighting, enabling the extraction of more comprehensive music emotion characteristics. Additionally, to enhance the accuracy of predicting musical emotion while reducing computational complexity during training phase, we consolidate Long short term memory network with Broad learning system network. We employ long short term memory structure as the feature mapping node of broad learning system structure, leveraging the advantages of both network models. This novel Neural Network architecture, called BLNN (Broad-Long Neural Network), achieves higher prediction accuracy. i.e., 66.78%, than single network models and other benchmark with/without consolidation methods. Moreover, it achieves lower time complexity than other excellent models, i.e., 169.32 s of training time and 507.69 ms of inference time, and achieves the optimal balance between efficiency and performance. In short, the extensive experimental results demonstrate that the proposed BLNN architecture effectively predicts music emotion, surpassing other models in terms of accuracy while reducing computational demands. In addition, the detailed description of the related work, along with an analysis of its advantages and disadvantages, and its future prospects, can serve as a valuable reference for future researchers. [ABSTRACT FROM AUTHOR]

Background: As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient's health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods: A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient's recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient's health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient's sensitive information in the blockchain network. Results: The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient's well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient's sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals. Conclusions: The proposed mechanism informs proactively the emergency team of professional clinicians about patients' critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users' trust to the access control mechanism. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the effect of blue light and its brightness on long-term memory. Blue light was tested under four distinct illuminance conditions ranging from 400 to 1000 lux, and the results were compared with those of red light as a comparison group. A total of 25 individuals, including 16 men and 9 women with a mean age of 22.04 ± 1.74 years, participated in these tests based on a within-subject research design. Initially, each participant was exposed to individual light conditions for 30 min in the morning. Subsequently, they were asked to complete learning memory tasks, which involved memorizing specific words composed of randomly chosen English consonants. Exactly 20 min after these memory tasks, their long-term memory retention was measured through recall. It was found that blue light was superior to red light in terms of long-term memory enhancement; the difference was statistically significant, with a confidence level of 95%. Moreover, brighter light environments were found to affect long-term memory performance more significantly compared to darker light environments. That said, there was no significant difference in interaction effects between light color and brightness. These findings confirm the results of previous studies highlighting the effectiveness of blue light in enhancing memory retention while also demonstrating that the degree of enhancement may vary depending on light brightness. [ABSTRACT FROM AUTHOR]

The article "Memory and Amnesia" in Frontiers in Education discusses a lesson plan for 7th and 8th-grade students to learn about different types of memory and amnesia. The lesson includes activities such as a working memory game, matching memories to their classifications, and analyzing memory loss in mock medical cases. The lesson aims to help students understand the various forms of memory and the causes of memory problems like amnesia. [Extracted from the article]

Users comment on hot topics on microblog using rhetorical techniques such as irony and sarcasm. Irony and sarcasm themselves carry a certain emotional tendency, which has a certain tendency to affect the sentiment analysis results. Therefore, this paper focuses on irony recognition of Chinese microblog comments, constructs a tri-classified dataset containing ironic, sarcasm and non- irony, and proposes a model BERT&lowbar;BiLSTM based on bidirectional encoder representations from Transformers (BERT) and bidirectional long short- term memory network (BiLSTM). The model generates dynamic word vectors containing contextual information through BERT, inputs BiLSTM to extract the deep ironic features of the text, and passes in softmax at the fully connected layer for ironic recognition of the text. The experimental results indicate that the BERT&lowbar;BiLSTM model proposed in this paper has significantly improved the accuracy and F1 values compared with the existing mainstream models on both binary and triple classification datasets. [ABSTRACT FROM AUTHOR]

Memory consolidation involves repeated replay of new information by the hippocampus, which transfers memories to the neocortex for long-term storage. This occurs mainly during slow wave sleep, a phase characterized in the cortex by low cholinergic tone and low afferent input. High cholinergic tone has been shown to hamper memory consolidation, probably mediated by reduced network excitability (the ease of activity propagation in a network). We used cortical neuronal networks on multi electrode arrays to investigate whether low background input contributes to memory consolidation. Networks received focal electrical stimuli to memorize, with or without background afferent input (global optogenetic stimulation). Background stimulation hampered memory formation and consolidation, confirming the importance of low background input. Moreover, it lowered network excitability, similar to high cholinergic tone. These findings suggest that high network excitability is a critical feature of slow wave sleep that facilitates memory consolidation. [ABSTRACT FROM AUTHOR]

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We provide two biologically plausible implementations for this reset that propose functional roles in stabilizing consolidation for core elements of the cerebellar circuit. [ABSTRACT FROM AUTHOR]

During ongoing narratives, event boundaries trigger processes relevant for subsequent memory. Previous work has shown that novel, unrelated input presented at an event boundary can retroactively interfere with short-term retention of the preceding event. This interference was attributed to a perturbation of offset-related processes taking place within seconds after encoding and supporting the binding of elements into a coherent event memory. However, the temporal specificity of this memory interference and whether its impact extends to longer retention delays has not been addressed. Here, participants viewed either individual or pairs of short narrative movie clips. Susceptibility to interference at event boundaries was probed by presenting the second clip either immediately after the first, or with a 2s encoding delay. In free and cued recall, after 20 min and 24 h, only memory for movie clips that were immediately followed by a second clip was reduced compared to clips shown in isolation. Intact offset-related processes (as indexed by successful recall of the first movie) did not negatively affect encoding of the subsequent clip. Together, these results indicate that the 2s time-window immediately after an event is relevant for successful consolidation and long-term retention of memory. [ABSTRACT FROM AUTHOR]

Benefits of self-testing for learning have been consistently shown for simple materials such as word lists learned by rote memorization. Considerably less evidence for such benefits exists for complex, more educationally relevant materials and its application to new situations. The present study explores the mechanisms underlying this transfer. To this end, a typical retrieval-practice-effect paradigm was applied to foster the learning of an artificial language. Participants either repeatedly studied grammatically correct exemplar sentences of the artificial language or engaged in a cloze test as the interim test after learning. To assess far transfer, participants in both groups of restudy and retrieval practice engaged in a grammaticality judgment test after a delay of 5 min and 1 week. In addition, participants in both groups completed a final memory test (i.e., a cloze test identical to the initial test) 1 week after learning. In addition to a long-term memory benefit of retrieval practice, results revealed also a retrieval-practice benefit in the far-transfer test after the 1-week delay. The findings further support the view that far transfer is supported by learning the underlying grammatical rules as opposed to memorizing the material. Thus, retrieval practice is also effective for fostering learning of complex materials and, even more importantly, for promoting transfer of learning—a crucial goal in modern educational practices. [ABSTRACT FROM AUTHOR]

Aiming at the sequential and nonlinear characteristics of power quality data over a long time span, a set of PQ evaluation and early warning system on DG distribution network based on deep learning is proposed. The intelligent power distribution network power quality monitoring and early warning system aims to realize the monitoring and forecasting and early warning functions of multiple indicators of power quality in the distribution network. First, we use the sliding window to convert the power quality data into a number of square graphs with time as the scale; second, we use the feature extraction advantage of (Convolutional Neural Network, CNN) to extract the features of each square graph sample and extract it. The characteristic information is transformed into the input of (Long Short Term Memory, LSTM) in a time series sequence; finally, according to the output of CNN-LSTM is used to complete the power quality data prediction of the active distribution network. Through an IEEE 13-node active distribution network simulation example with distributed power sources, this method decouples the feature extraction, analysis, and prediction tasks of power quality data and simplifies the prediction work. Compared with the selected control model, it is significantly to improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

In contrast to language production, there are few comprehension models of the representation and use of grammatical gender in long-term memory. To bridge this gap, we conducted a systematic review of empirical studies on the role of gender-form regularities in the recognition of nouns in isolation and within sentences. The results of a final sample of 40 studies suggest that there are two routes for the retrieval of gender during real-time comprehension: a form-based route and a lexical-based route. Our review indicates that the use of these routes depends on the degree of gender transparency of the language and the degree of overtness of the experimental paradigm. To accommodate these findings, we incorporate a dual-route mechanism within a general model of lexical access in comprehension, the AUSTRAL (Activation Using Structurally Tiered Representations and Lemmas) model, and identify directions for future research. [ABSTRACT FROM AUTHOR]

To address the issues of sparse training data, complex entity structures, and uneven entity distribution in the industrial equipment failure domain, this study constructs an industrial equipment failure named entity recognition corpus. Due to the difficulty of character level named entity recognition models in representing the professional vocabulary information in the field of industrial equipment failure, this study proposes a character enhanced industrial equipment failure named entity recognition model to address this problem. In the embedding layer, professional vocabulary information is directly fused between the Transformer layers of ROBERT WWM (Robustly Optimized BERT Pretraining Approach with Whole Word Masking) to allocate word information to each of its constituent characters for enhanced semantics. The global semantic information is obtained through a BiLSTM (Bidirectional Long Short Term Memory), and the CRF (Conditional Random Field) is used to learn the dependency relationship between adjacent labels to obtain the optimal sentence level label sequence. Experimental results demonstrate that the proposed model has good performance on industrial equipment fault named entity recognition tasks, with an average F1 score of 92.403%. [ABSTRACT FROM AUTHOR]

Photodynamic therapy (PDT) is a clinically approved therapeutic modality for treating oncological and non-oncological disorders. PDT has proclaimed multiple benefits over further traditional cancer therapies including its minimal systemic toxicity and selective ability to eliminate irradiated tumors. In PDT, a photosensitizing substance localizes in tumor tissues and becomes active when exposed to a particular wavelength of laser light. This produces reactive oxygen species (ROS), which induce neoplastic cells to die and lead to the regression of tumors. The contributions of ROS to PDT-induced tumor destruction are described by three basic processes including direct or indirect cell death, vascular destruction, and immunogenic cell death. However, the efficiency of PDT is significantly limited by the inherent nature and tumor microenvironment. Combining immunotherapy with PDT has recently been shown to improve tumor immunogenicity while decreasing immunoregulatory repression, thereby gently modifying the anticancer immune response with long-term immunological memory effects. This review highlights the fundamental ideas, essential elements, and mechanisms of PDT as well as nanomaterial-based PDT to boost tumor immunogenicity. Moreover, the synergistic use of immunotherapy in combination with PDT to enhance immune responses against tumors is emphasized. [ABSTRACT FROM AUTHOR]

This study investigated the development of divergent thinking (DT) in early childhood. We followed 107 4-year-olds for 1.5 years. Children's DT was assessed with the Alternative Uses Task (AUT) every 6 months, four times in total. Within the AUT, children were asked to generate unusual uses of common objects while explaining how they came up with these uses. Based on the generated uses, two product measures were computed: fluency and originality. We also applied process measures to gain insight into the thinking processes children used to come up with uses. Based on the coded data of children's verbal explanations of how they generated uses, two process measures were computed: memory retrieval (retrieving uses from episodic and semantic long-term memory) and mental operations (performing mental operations on the object to generate a use). The results revealed substantial growth in fluency and originality with age. The frequencies of occurrence of both memory retrieval and mental operations also increased with age. In line with previous findings, this longitudinal study shows that creativity develops throughout early childhood. Moreover, this study adds to the current knowledge base by providing researchers a first glance at the development of thinking processes underlying DT in this age range. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that constraints can facilitate creative thinking rather than hinder it. This research tested how individual differences in working memory capacity (WMC) affect the creativity of ideas generated under conditions of low and high constraint. Participants generated short sentences with either low or high constraints placed on the choice of initial letters used. Under the high-constraint condition, participants were less likely to successfully produce sentences but the sentences were judged as more creative. There was an interaction between constraint condition and working memory capacity such that WMC and creativity were positively associated in the low constraint condition, but not in the high constraint condition. An examination of strategy reports also indicated that higher WMC participants may have made better use of strategies which were related to higher creativity in the low-constraint condition. This research adds to findings that constraints can be beneficial for creativity, and is consistent with suggestions that WMC enhances efficient search and retrieval from long-term memory and use of strategies. [ABSTRACT FROM AUTHOR]

We commonly load visual working memory minimally when to-be-remembered information remains available in the external world. In visual search, this is characterised by participants frequently resampling previously encoded templates, which helps minimize cognitive effort and improves task performance. If all search templates have been rehearsed many times, they should become strongly represented in memory, possibly eliminating the benefit of reinspections. To test whether repetition indeed leads to less resampling, participants searched for sets of 1, 2, and 4 continuously available search templates. Critically, each unique set of templates was repeated 25 trials consecutively. Although the number of inspections and inspection durations initially decreased strongly when a template set was repeated, behaviour largely stabilised between the tenth and last repetition: Participants kept resampling templates frequently. In Experiment 2, participants performed the same task, but templates became unavailable after 15 repetitions. Strikingly, accuracy remained high even when templates could not be inspected, suggesting that resampling was not strictly necessary in later repetitions. We further show that seemingly 'excessive' resampling behaviour had no direct within-trial benefit to speed nor accuracy, and did not improve performance on long-term memory tests. Rather, we argue that resampling was partially used to boost metacognitive confidence regarding memory representations. As such, eliminating the benefit of minimizing working memory load does not eliminate the persistence with which we sample information from the external world – although the underlying reason for resampling behaviour may be different. [ABSTRACT FROM AUTHOR]

Categorical search involves looking for objects based on category information from long-term memory. Previous research has shown that search efficiency in categorical search is influenced by target/distractor similarity and category variability (i.e., heterogeneity). However, the interaction between these factors and their impact on different subprocesses of search remains unclear. This study examined the effects of target/distractor similarity and category variability on processes of categorical search. Using multidimensional scaling, we manipulated target/distractor similarity and measured category variability for target categories that participants searched for. Eye-tracking data were collected to examine attentional guidance and target verification. The results demonstrated that the effect of category variability on response times (RTs) was dependent on the level of target/distractor similarity. Specifically, when distractors were highly similar to target categories, there was a negative relation between RTs and variability, with low variability categories producing longer RTs than higher variability categories. Surprisingly, this trend was only present in the eye-tracking measures of target verification but not attentional guidance. Our results suggest that searchers more effectively guide attention to low-variability categories compared to high-variability categories, regardless of the degree of similarity between targets and distractors. However, low category variability interferes with target match decisions when distractors are highly similar to the category, thus the advantage that low category variability provides to searchers is not equal across processes of search. [ABSTRACT FROM AUTHOR]

Perineuronal nets (PNNs), a specialized form of extra cellular matrix (ECM), surround numerous neurons in the CNS and allow synaptic connectivity through holes in its structure. We hypothesize that PNNs serve as gatekeepers that guard and protect synaptic territory and thus may stabilize an engram circuit. We present high-resolution and 3D EM images of PNN-engulfed neurons in mice brains, showing that synapses occupy the PNN holes and that invasion of other cellular components is rare. PNN constituents in mice brains are long-lived and can be eroded faster in an enriched environment, while synaptic proteins have a high turnover rate. Preventing PNN erosion by using pharmacological inhibition of PNN-modifying proteases or matrix metalloproteases 9 (MMP9) knockout mice allowed normal fear memory acquisition but diminished long-term memory stabilization, supporting the above hypothesis. [ABSTRACT FROM AUTHOR]

Stereotyped brain asymmetry, also known as laterality, is widespread and multiscale, encompassing asymmetrical molecular function, left–right (LR) asymmetrical neurons or circuits, structural differences, and lateralized behaviors. Brain laterality can modulate a wide variety of behaviors and cognitive functions, including sleep, memory, food preference, fear, and anxiety. Long-term memory is among the most evolutionary conserved cognitive traits influenced by brain lateralization. The mechanisms and molecular pathways governing brain laterality vary widely among model organisms. The current limited understanding of the diversity of asymmetries hinders the definition of common principles and conserved symmetry-breaking mechanisms. The concordance between body and brain LR asymmetry can be strong, partial, or completely absent, raising questions about the emergence and coordination of asymmetry between the brain and body. Chirality is a fundamental trait of living organisms, encompassing the homochirality of biological molecules and the left–right (LR) asymmetry of visceral organs and the brain. The nervous system in bilaterian organisms displays a lateralized organization characterized by the presence of asymmetrical neuronal circuits and brain functions that are predominantly localized within one hemisphere. Although body asymmetry is relatively well understood, and exhibits robust phenotypic expression and regulation via conserved molecular mechanisms across phyla, current findings indicate that the asymmetry of the nervous system displays greater phenotypic, genetic, and evolutionary variability. In this review we explore the use of nematode, zebrafish, and Drosophila genetic models to investigate neuronal circuit asymmetry. We discuss recent discoveries in the context of body–brain concordance and highlight the distinct characteristics of nervous system asymmetry and its cognitive correlates. [ABSTRACT FROM AUTHOR]

Introduction: Learning is a long-term memory process heavily influenced by the control processes implemented by working memory, including recognition of semantic properties of items by which subjects generate a semantic structure of engrams. Aim: The aim of this study is to investigate the verbal learning strategies of patients affected by a tumor in the left frontal lobe to highlight the role of area 9. Method: Ten patients with frontal low-grade gliomas and ten healthy control subjects, matched for age, sex and education, were recruited and then evaluated with a two-part verbal learning test: multi-trial word list learning in free recall, and multi-trial word list learning preceded by an explicit semantic strategy cue. Frontal patients were divided into two groups: those either with frontal lesions involving or sparing area 9. Results: In comparison to healthy control subjects, frontal patients with lesions involving area 9 memorized fewer words and displayed difficulty in using semantic strategies. When the strategy was suggested by the examiner, their performance improved, but to a lesser extent than the healthy control. Conversely, frontal patients with lesions sparing area 9 showed similar results to healthy control subjects. Conclusion: The results suggested that, while the identification of the categorical criterion requires the integrity of the entire dorsolateral prefrontal area, only area 9, and not the surrounding areas, could be responsible for the effective use of semantic strategies in learning tasks. [ABSTRACT FROM AUTHOR]